7.2 Drive Cycle Simulation Results
7.2.3 Effect of Driving Style
One final note to make is regarding driving style; to apply results to real applications it should be noted that driving style can significantly affect the required load on the battery pack. In the previous drive cycle analyses, only the FTP drive cycle was considered. The FTP-75 drive cycle is known to have some shortcomings in representing true driving conditions, namely a lack of high acceleration and high speeds [70, 71]. Other drive cycles have been developed in an attempt to make up for this shortcoming [26, 29]. Some of these other standard drive cycles are shown in Table 7-5.
As seen in Table 7-5, the other drive cycles used have more demanding energy requirements, and represent different driving styles. The LA92 drive cycle is very similar to the FTP drive cycle, just with overall higher speeds, less idling, and higher acceleration. US06 is a very intense but short driving cycle with very high speeds and accelerations. The US06 drive cycle was developed, as a supplemental cycle to the FTP cycle as it was believed the FTP drive cycle was lacking in representing real world aggressive driving [71]. Figure 7-15 now shows a comparison of the average and maximum C-rates generated by other drive cycles. In this analysis, the FTP+US06 drive cycle was generated to reflect a period of calmer city driving to
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a period of intense highway driving. The 2xUS06 drive cycle is merely two consecutive US06 drive cycles; this was done to have an intense drive cycle with a more comparable time period to the FTP cycle.
Table 7-5: Drive cycle characteristics [65]
Drive Cycle Duration(s) Distance(km) Average speed(km/hr) Top speed(km/hr)
FTP 1875 17.8 34.1 91.2
US06 601 12.9 77.8 129.2
LA92 1436 15.8 39.6 108.1
FTP+US06 2476 30.7 44.7 100.4
2 US06 1202 25.8 77.8 129.2
Figure 7-15: Comparison of C-rates for different drive cycles; data taken at 20°C with h = 6.3 W/m2K, charge voltage of 4.2V
As seen by Figure 7-15, driving style can have a large impact on the required battery load, and hence the C-rate. As concluded earlier, namely in Table 7-1, there is a significantly higher need for effective thermal management with higher C-rates. Therefore, it is expected that more intense driving styles will result in a higher need for effective cooling strategies.
Figure 7-16 and Figure 7-17 show the average battery temperature and maximum battery temporal temperature change under the different drive cycles and different heat transfer coefficients with an initial temperature of 35°C.
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Figure 7-16: Average temporal cell temperature for a single drive cycle with varying heat transfer coefficients; initial temperature of 35°C
Figure 7-17: Maximum cell temperature over time for a single drive cycle with varying heat transfer coefficients; initial temperature of 35°C
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Observing the above plots clearly shows the need for effective thermal management. While the FTP drive cycle shows minimal temperature increase, other drive cycle displays a considerable increase in cell temperature over a single drive cycle. However, with effective thermal management (as described with h = 340 W/m3) the effects of even severe and aggressive driving can be mitigated. For instance, the US06 drive cycle which represents high intensity driving for 10 minutes, a very realistic case, would result in an average cell temperature of 4°C higher than the base temperature and a maximum temperature almost 10°C higher than the base, with no cooling system. With effective cooling this can be reduced to a 1°C increase in average temperature and only a 2°C maximum increase. As a reminder, heat generation was seen to be even more severe in 20°C, making thermal management systems even more useful.
The last result to reiterate is how much driving habits can influence a need for effective thermal management can be seen by observing heat generation rates for the different drive cycles in Figure 7-18.
Figure 7-18: Average volumetric heat generation during a single drive cycle; h = 340 W/m3 One final note to make is regarding environmental conditions. Effective thermal management must also be utilized to combat environmental conditions. In this study, the initial temperature of the battery was always set to the same temperature of the coolant. In reality, environmental conditions must also be taken into account when considering the thermal condition for a battery. For instance, if the desired battery temperature is 25°C, not only must temperature increase due to battery heat generation be mitigated, but if this vehicle is operating in a warm region (such as the southern United States) environmental conditions will also lend to increasing battery temperature.
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Over this results section, the effect of thermal management system on lithium-ion battery capacity fade has been presented, and the need for effective thermal management has been demonstrated. This information is very useful for the industry partner who wishes to demonstrate to customers the need for effective cooling (ICE plate employing a coolant) opposed to simpler thermal management systems.
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8 Conclusions
The section will reiterate the conclusions made during the course of this study.
Firstly, AutoLionST has shown to be a useful tool to model the operation and ageing of lithium-ion batteries. Inclusion of film formation and active material loss degradation mechanisms allow reflect the major ageing mechanisms in lithium-ion batteries. The only time these mechanisms alone are not suitable is at very low temperatures where lithium plating becomes the dominant contributor to battery degradation. While it is possible to adjust degradation parameters to give acceptable results at lower temperatures (20°C), it is not recommended to perform extended degradation testing at very low temperatures with this software, since the true degradation mechanisms are not being reflected.
As expected higher temperatures provide a higher initial capacity, however elevated temperatures (namely above 30°C) will result in much accelerated ageing.
Through the simulation of constant C-rate operation, it was shown that a choice of effective thermal management can significantly improve battery life. The importance of thermal management is most crucial at high C-rate operation, and the need to prevent battery temperature rise is most important at desirable battery temperatures (20-30°C).
Upon performing simulations employing FTP drive cycles, further conclusions can be drawn regarding battery performance against the variables of cell temperature, charge voltage, and heat transfer coefficient. Of the three variables, temperature again has the largest effect on capacity fade, a result which was expected following the result of the isothermal simulations.
Again, a high heat transfer can significantly improve battery life, especially at desirable temperatures, with approximately 25% improvement in battery life. It was also shown by including regenerative braking into the model that a lower charge voltage will also contribute to improved battery life, and that using regenerative braking, these lower charge voltages can be used with less risk of reaching the lower voltage limit of the battery pack.
Following the drive cycle capacity fade simulations, the effect of different drive cycles on the cell operating temperature and heat generation was explored. It was shown that the FTP drive cycle represents very low intensity driving, and that other drive cycles, which better reflect certain real life driving conditions, result in increased heat generation and large increases in cell temperature during operation. As shown in earlier results, heat generation and significant temperature increase will lead to enhanced battery degradation. Therefore, it was shown that while thermal management plays an even more significant role when considering high intensity driving. Hence, in electric vehicles thermal management is important not only to combat environmental conditions, but is also crucial to battle the heat generation of the battery pack.
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9 Future Work
The following is a brief discussion of the future work which could follow this study.
Firstly, further experimental experimentation would be very beneficial in order to ensure the model is properly calibrated to experimental data. Ideally this would include extended testing, hopefully to around 1000 cycles per cell, at multiple temperatures, with replication for each temperature (multiple cells cycled per temperature). Having such thorough experimental data would give a good measure of the variance between batteries when it comes to real life use, hence leading to more realistic model prediction once this data is used to calibrate the model.
As well, it is desired to extend results to capture the effect of temperature gradients on capacity fade of lithium-ion batteries. In many cases thermal gradients will exist both across the face of lithium-ion batteries, and through their thickness. As seen in the literature review, many researchers have documented that overall battery temperature has an effect on capacity fade, however no literature exists documenting the effect that non-uniformity of temperature has on capacity fade. For instance, if two batteries are both at an average temperature of 35°C, with one having a uniform temperature distribution, and one having minimum/maximum temperatures of 25°C and 40°C, respectively, how would their degradation vary? As a direct extension of the work presented in this study, the temperature gradients which are created for different thermal management systems can be studied, and the effect of these gradients on degradation can be explored.
If AutoLionST were to be used to study the effect of temperature gradients, multiple batteries at varying temperatures could be simulation in parallel, together representing different temperature zones of a single battery. This would be necessary since AutoLionST does not discretize temperature modeling. This work could also involve employing other AutoLion software, such as their 1D or 3D software which are Fluent based (opposed to MATLAB based).
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Appendix A: Full List of AutoLionST Battery Parameters
Table A-1: Cell dimensions
Table A-2: Positive electrode material parameters and properties [64]
Positive Electrode
Foil Parameters Value Units Status
Material Aluminum - Selected
Thickness 15 µm Default
Width 59.7 mm Default
Density 2.7 g/cm3 Default
Conductivity 3.538*107 S/m Default
Active Material Parameters Value Units Status
Material NMC - Selected
Molecular Weight 96.461 g/cm3 Material Constant
Density 4.8 g/cm3 Material Constant
1st Charge Capacity 163 mAh/g Material Constant
1st Discharge Capacity 153 mAh/g Material Constant
Umax 4.3 V Material Constant
Particle Size 10 µm Material Constant
Weight Percentage 94% - Default
Conductive Agent Parameters Value Units Status
Material Carbon - Default
Density 1.95 g/cm3 Default
Weight Percentage 3% - Default
Binder Parameters Value Units Status
Material PVdF - Default
Density 1.77 g/cm3 Default
Weight Percentage 3% - Default
Coating Parameters Value Units Status
Loading 3.9 mAh/cm2 Selected
Electrode Thickness 170 µm Selected
Electrode Width 49 mm Selected
Electrode Height 144 mm Selected
# of Electrode Plates 20 - Calculated
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Table A-3: Negative electrode material parameters and properties [64]
Negative Electrode
Foil Parameters Value Units Status
Material Copper - Selected
Thickness 8 µm Default
Width 59.7 mm Default
Density 8.96 g/cm3 Default
Conductivity 5.8*107 S/m Default
Active Material Parameters Value Units Status
Material Graphite - Selected
Molecular Weight 72.06 g/cm3 Material Constant
Density 2.24 g/cm3 Material Constant
1st Charge Capacity 371.933 mAh/g Material Constant
1st Discharge Capacity 350 mAh/g Material Constant
Umax 2 V Material Constant
Particle Size 15 µm Material Constant
Weight Percentage 94% - Default
Conductive Agent Parameters Value Units Status
Material Carbon - Default
Density 1.95 g/cm3 Default
Weight Percentage 3% - Default
Binder Parameters Value Units Status
Material PVdF - Default
Density 1.77 g/cm3 Default
Weight Percentage 3% - Default
Coating Parameters Value Units Status
N/P Ratio 115% - Selected
Loading 4.485 mAh/cm2 Calculated
Electrode Thickness 170 µm Selected
Electrode Width 49 mm Selected
Electrode Height 144 mm Selected
# of Electrode Plates 21 - Calculated
Table A-4: Separator parameters [64]
Separator Parameters Value Units Status
Type Celgard - Default
Thickness 20 µm Default
Height 145 mm Selected
Porosity 0.4 - Default
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Table A-5: Electrolyte Parameters [64]
Electrolyte Parameters Value Units Status
Lithium Salt LiPF6 - Default
Solution EC-EMC-DMC - Default
Concentration 1.2 mol/L Default
Density 1.2 g/cm3 Default
Table A-6: Cell specifications [64]
Cell Specifications Value Units Status
Cell Surface Area 243.24 cm2 Calculated
Cell Volume 106.92 cm3 Calculated
Negative Electrode Binder/Additive/Conductive Agent 2.28 Calculated Positive Electrode Binder/Additive/Conductive Agent 4.31 Calculated
Negative Current Collector 10.62 Calculated
Positive Current Collector 5.72 Calculated
Enclosure 51.05 Selected
Table A-8: Mesh parameters [64]
Mesh Number Parameters Value Status
Negative Electrode 8 Default (verified with mesh refinement)
Separator 5 Default (verified with mesh refinement)
Positive Electrode 8 Default (verified with mesh refinement)
Table A-9: Operating conditions [64]
Operating Conditions Value (V) Status
Lower Cut-off Voltage 2.75 Selected
Lower Cut-off Voltage 2.75 Selected